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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 BaronePLOSCompBio2017 13-11.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators|Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principal investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work, including high-performance computing (HPC), [[bioinformatics]] support, multistep workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the United States and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC, acknowledging that data science skills will be required to build a deeper understanding of life. This portends a growing data knowledge gap in biology and challenges institutions and funding agencies to redouble their support for computational training in biology. ('''[[Journal:Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators|Full article...]]''')<br />
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Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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